During televised events such as sporting events, reality shows, and other presentations, sponsors often use sponsorship advertising to market their products. Sponsorship advertising can take on a variety of forms, such as fixed signage, audio or visual graphics, apparel advertising, and the like. Unlike valuing a 30-second spot commercial, attempting to value sponsorship advertising has historically been a difficult, subjective, and uncertain process.
The world of advertising revolves precise duration horizons, such as the 30-second spot commercial. Almost all valuations of all forms of advertising stem from or are a function of the 30-second spot rate, which is the price paid by a company for a 30-second commercial. If a sponsor is unable to definitively and objectively ascertain metrics related to advertising data, then it will not be able to reliably value such data. These data elements, or metrics, would be important and useful various entities, such as the sponsor, an event presenter, networks or other rights holder.
A technique to try to determine occurrences of sponsorship information is to employ image-recognition technology. For example, Taylor Nelson Sofres plc of Westgate, London employs software developed by Lucent Technologies Inc. of Murray Hill, N.J. to attempt to match logos to templates. Also Margaux Matrix Limited of Godalming, Surry in Great Britain employs image-recognition technology to help identify advertising images. But the level of accuracy demanded in the industry is often too high to be met by the results obtained from image-recognition technology, which is often constrained to comparing image captures to provided templates.
Another shortcoming associated with image-recognition technology is overcoming the inherent difficulty of automatically determining the source of the signage. Signage may appear behind home plate at a baseball game, on the ball cap of a golfer, on the back wall of a stadium, on an automobile door, or in any other of almost innumerable places. Signage can also appear as a graphic (a computer animated object that appears on a viewer's television screen). No image-recognition technology has been demonstrated to accurately identify the source, distinguishing it from other sources. For instance, consider baseball stadium where the outfield is bounded by a wall with blue background, upon which is fixed a first logo. Consider a second logo that appears behind home plate, but is also on a blue background. If a camera were to zoom in on one of the logos (to capture a great catch in the outfield, or a missed catch by the catcher for example), then all that could be seen is the logo surrounded by blue. Finally, image-recognition technology often creates false hits (father positives or false negatives) that must be dealt with.
Thus, current techniques suffer from at least the exemplary shortcomings listed above, and also do not employ any form of detection indexing to arrive at an overall valuation factor or score related to sponsorship-advertising. Unfortunately, all detections are valued equally, even though some detections have greater impact potential than others. With an inability to quantify the potential impact associated with advertising seen during an event, decision makers are ill-equipped to determine the value of sponsorship advertising. The present state of the art could therefore be improved by providing a method and system for accurately and quantitatively valuing the potential impact of advertising content.